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Present attack methods can make state-of-the-art classification systems based on deep neural networks misclassify every adversarially modified test example.
De La Escalera, A., Moreno, L.E., Salichs, M.A., Armingol, J.M.: Road traffic sign detection and classification. IEEE transactions on industrial electronics 44
1997
Earlier work this paper cites.
Javed, O., Shah, M.: Tracking and object classification for automated surveillance. ECCV (2006)
2006
Earlier work this paper cites.
Biggio, B., Fumera, G., Roli, F.: Adversarial pattern classification using multiple classifiers and randomisation. Structural, Syntactic, and Statistical Pattern Recognition (2008)
2008
Earlier work this paper cites.
Paruchuri, P., Pearce, J.P., Marecki, J., Tambe, M., Ordonez, F., Kraus, S.: Playing games for security: An efficient exact algorithm for solving bayesian stackelberg games. In: AAMAS (2008)
2008
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp. 1097–1105 (2012)
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Vorobeychik, Y., Li, B.: Optimal randomized classification in adversarial settings. In: AAMAS (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Du, Y., Wang, W., Wang, L.: Hierarchical recurrent neural network for skeleton based action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1110–1118 (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal of Computer Vision 115
2015
Earlier work this paper cites.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: CVPR (2015)
2015
Cited alongside, same era.
Taguinod, M., Doupé, A., Zhao, Z., Ahn, G.J.: Toward a Moving Target Defense for Web Applications. In: IEEE Information Reuse and Integration (IRI) (2015)
2015
Cited alongside, same era.
Bastani, O., Ioannou, Y., Lampropoulos, L., Vytiniotis, D., Nori, A., Criminisi, A.: Measuring neural net robustness with constraints. In: NIPS (2016)
2016
Cited alongside, same era.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Cited alongside, same era.
2016
2017
Closest in time.
Brown, T.B., Mané, D., Roy, A., Abadi, M., Gilmer, J.: Adversarial patch. arXiv:1712.09665 (2017)
2017
Closest in time.
Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: IEEE S&P (2017)
2017
Closest in time.
2017
Closest in time.
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Cited alongside, same era.
Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR (2016)
2016
Cited alongside, same era.
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: EuroS&P. pp. 372–387. IEEE (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: Distillation as a defense to adversarial perturbations against deep neural networks. In: IEEE S&P (2016)
2016
Cited alongside, same era.
Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M.K.: Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition. In: Proceedings of the SIGSAC Conference on Computer and Communications Security. ACM (2016)
2016
Cited alongside, same era.
Zheng, S., Song, Y., Leung, T., Goodfellow, I.: Improving the robustness of deep neural networks via stability training. In: CVPR (2016)
2016
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Closest in time.
2017
Closest in time.
2017
Closest in time.
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against machine learning. In: ACM CCS (2017)
2017
Closest in time.
Sengupta, S., Vadlamudi, S.G., Kambhampati, S., Doupé, A., Zhao, Z., Taguinod, M., Ahn, G.J.: A game theoretic approach to strategy generation for moving target defense in web applications. AAMAS (2017)
2017
Closest in time.
2017
Closest in time.
2018
Closest in time.